Search results for: intelligent multiplication table

Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

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Authors: Siwei Chang, Heng Li, Haitao Wu, Xin Fang

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Construction robots are receiving more and more attention as a promising solution to the manpower shortage issue in the construction industry. The development of intelligent control techniques that assist in controlling the robots to avoid transparency and reflected building obstacles is crucial for guaranteeing the adaptability and flexibility of mobile construction robots in complex construction environments. With the boom of computer vision techniques, a number of studies have proposed vision-based methods for transparent obstacle avoidance to improve operation accuracy. However, vision-based methods are also associated with disadvantages such as high computational costs. To provide better perception and value evaluation, this study aims to analyze the performance of vision-based techniques for avoiding transparent building obstacles. To achieve this, commonly used sensors, including a lidar, an ultrasonic sensor, and a USB camera, are equipped on the robotic platform to detect obstacles. A Raspberry Pi 3 computer board is employed to compute data collecting and control algorithms. The turtlebot3 burger is employed to test the programs. On-site experiments are carried out to observe the performance in terms of success rate and detection distance. Control variables include obstacle shapes and environmental conditions. The findings contribute to demonstrating how effectively vision-based obstacle avoidance strategies for transparent building obstacle avoidance and provide insights and informed knowledge when introducing computer vision techniques in the aforementioned domain.

Keywords: construction robot, obstacle avoidance, computer vision, transparent obstacle

Procedia PDF Downloads 80

Authors: Mohammad Mirwais Yusufi

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Skillful management is one of the most important success factors for today’s farms. When a farm is well managed, it can generate funds for its sustainability. Grape is one of the most diffused fruits in the world and one of the most important cash crops with high potential of production in Afghanistan as well. While there are several organizations intervening for improvement of this cash crop, the quality and quantity are still not satisfactory for producers and external markets. The situation has not changed over the years. Therefore, a survey was conducted in 2017 with 60 grape growers, supported by questionnaires in Mirbachakot, Kalakan and Shakardara districts of Kabul province. The purpose was to get an understanding of the current socio-demographic characteristics of farmers, management methods, constraints, farm size, yield and contribution of grape farming to household income. Findings indicate that grape farming was predominant 83.3% male, 16.6% female and small-scale farmers were the main grape producers, 60% < 1 ha of land under grape production. Likewise, 50% had more than > 10 years and 33.3% between 1-5 years’ experience in grape farming. The high level of illiteracy and diseases had significant digit effect on growth, yield and quality of grapes. The results showed that vineyard management operations to protect grapes from mechanical damage are very poor or completely absent. Comparing developed countries, table grape is one of the fruits with the highest input of technology, while in developing countries the cost of labor is low but the purchase of the equipment is very high due to financial situation. Hence the low quality and quantity of grape are influenced by poor management methods, such as non-availability of experts and lack of technical guidance in the study site. Thereby, the study suggested that improved agricultural extension services and managerial skills could contribute to addressing the problems.

Keywords: constraints, effect, management, Kabul

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Authors: Velimir Monty Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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In general, state-of-the-art Data Acquisition Systems (DAQ) in high energy physics experiments must satisfy high requirements in terms of reliability, efficiency and data rate capability. This paper presents the development and deployment of a debugging tool named DAQ Debugger for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. Utilizing a hardware event builder, the iFDAQ is designed to be able to readout data at the average maximum rate of 1.5 GB/s of the experiment. In complex softwares, such as the iFDAQ, having thousands of lines of code, the debugging process is absolutely essential to reveal all software issues. Unfortunately, conventional debugging of the iFDAQ is not possible during the real data taking. The DAQ Debugger is a tool for identifying a problem, isolating the source of the problem, and then either correcting the problem or determining a way to work around it. It provides the layer for an easy integration to any process and has no impact on the process performance. Based on handling of system signals, the DAQ Debugger represents an alternative to conventional debuggers provided by most integrated development environments. Whenever problem occurs, it generates reports containing all necessary information important for a deeper investigation and analysis. The DAQ Debugger was fully incorporated to all processes in the iFDAQ during the run 2016. It helped to reveal remaining software issues and improved significantly the stability of the system in comparison with the previous run. In the paper, we present the DAQ Debugger from several insights and discuss it in a detailed way.

Keywords: DAQ Debugger, data acquisition system, FPGA, system signals, Qt framework

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Qasim HamaKhurshid HamaMurad, Normal Mat Jusoh, Uznir Ujang

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The concept of the city with an infrastructure of (information and communication) Technology embraces several definitions depending on the meanings of the word "smart" are (intelligent city, smart city, knowledge city, ubiquitous city, sustainable city, digital city). Many definitions of the city exist, but this chapter explores which one has been universally acknowledged. From literature analysis, it emerges that Smart City is the most used terminologies in literature through the digital database to indicate the smartness of a city. This paper share exploration the research from main seven website digital databases and journal about Smart City from "January 2015 to the February of 2020" to (a) Time research, to examine the causes of the Smart City phenomenon and other concept literature in the last five years (b) Review of words, to see how and where the smart city specification and relation different definition And(c) Geographical research to consider where Smart Cities' greatest concentrations are in the world and are Malaysia has interacting with the smart city, and (d) how many papers published from all Malaysia from 2015 to 2020 about smart citie. Three steps are followed to accomplish the goal. (1)The analysis covered publications Build a systematic literature review search strategy to gather a representative sub-set of papers on Smart City and other definitions utilizing (GoogleScholar, Elsevier, Scopus, ScienceDirect, IEEEXplore, WebofScience, Springer) January2015-February2020. (2)A bibliometric map was formed based on the bibliometric evaluation using the mapping technique VOSviewer to visualize differences. (3)VOSviewer application program was used to build initial clusters. The Map of Bibliometric Visualizes the analytical findings which targeted the word harmony.

Keywords: bibliometric research, smart city, ICT, VOSviewer, urban modernization

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: W. Thongwat, B. Terakulsatit

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The study area is Kham Sakaesaeng District in Nakhon Ratchasima Province, the south section of Northeastern Thailand, located in the Lower Khorat-Ubol Basin. This region is the one of saline soil area, located in a dry plateau and regularly experience standing with periods of floods and alternating with periods of drought. Especially, the drought in the summer season causes the major saline soil and saline water problems of this region. The general cause of dry land salting resulted from salting on irrigated land, and an excess of water leading to the rising water table in the aquifer. The purpose of this study is to determine the relationship of physical and chemical properties between the soil and groundwater. The soil and groundwater samples were collected in both rainy and summer seasons. The content of pH, electrical conductivity (EC), total dissolved solids (TDS), chloride and salinity were investigated. The experimental result of soil and groundwater samples show the slightly pH less than 7, EC (186 to 8,156 us/cm and 960 to 10,712 us/cm), TDS (93 to 3,940 ppm and 480 to 5,356 ppm), chloride content (45.58 to 4,177,015 mg/l and 227.90 to 9,216,736 mg/l), and salinity (0.07 to 4.82 ppt and 0.24 to 14.46 ppt) in the rainy and summer seasons, respectively. The distribution of chloride content and salinity content were interpolated and displayed as a map by using ArcMap 10.3 program, according to the season. The result of saline soil and brined groundwater in the study area were related to the low-lying topography, drought area, and salt-source exposure. Especially, the Rock Salt Member of Maha Sarakham Formation was exposed or lies near the ground surface in this study area. During the rainy season, salt was eroded or weathered from the salt-source rock formation and transported by surface flow or leached into the groundwater. In the dry season, the ground surface is dry enough resulting salt precipitates from the brined surface water or rises from the brined groundwater influencing the increasing content of chloride and salinity in the ground surface and groundwater.

Keywords: environmental geology, soil salinity, geochemistry, groundwater hydrology

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Dina Mohamed Ayman, Rafieu Akin

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In an attempt to increase the female total entrepreneurial activities (TEA) within Egypt and Nigeria, this paper aims to investigate the challenges facing female entrepreneurs operating in Egypt, in relative to Nigeria. In this regard, both researchers undertook a qualitative approach due to the scarcity of the literature reviewed on the topic; in those particular countries, and as an in-depth comparative mode. Therefore, ten Egyptian entrepreneurs in relative to ten Nigerian entrepreneurs were in-depth investigated. The research findings prove that female entrepreneurs face complex problems for being both gender and country-specific. Regarding the gender-specific obstacles, the work/life imbalance due to the scarcity of child-care nurseries and the prevalence of the gender-role division while performing the house chores rather than the concept of co-operation, acted as a main source of cultural challenge because women are considered mostly as 'housewives'. However, interestingly, this specific gender-discrimination challenge is proven to have no grounded effect in terms of the business-establishment and daily dealings neither in Egypt nor Nigeria, as one of the sample exclaimed 'as long as you pay, then no gender difference is set on the table'. Other country-specific challenges facing female entrepreneurs, lied in, the aggregate weak entrepreneurial framework governing both countries, also, women faced the difficulty of access to financial institutions with collateral requirements that are usually "hardly to be met", besides, the absence of the "micro-credit-Grameen-banks" concept. As well, the scarcity of incubators and business training centers providing network, consultancy and well-trained workforce to female entrepreneurs constitute a major hurdle for women entrepreneurs operating in both countries. Finally, this paper will conclude the research by offering a set of public-policy recommendations to pave the way for females to choose self-employment as a career path.

Keywords: entrepreneurship, female entrepreneurship, obstacles, framework conditions, culture, micro-credit

Procedia PDF Downloads 372

Authors: Hao Wang, Shuguo Pan

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The Global Navigation Satellite System (GNSS) signal received by the dynamic vehicle in the harsh environment will be frequently interfered with and blocked, which generates gross error affecting the positioning accuracy of the GNSS/Inertial Navigation System (INS) integrated navigation. Therefore, this paper put forward an improved robust Cubature Kalman filter (CKF) algorithm for single-frequency GNSS/INS tightly coupled system ambiguity resolution. Firstly, the dynamic model and measurement model of a single-frequency GNSS/INS tightly coupled system was established, and the method for GNSS integer ambiguity resolution with INS aided is studied. Then, we analyzed the influence of pseudo-range observation with gross error on GNSS/INS integrated positioning accuracy. To reduce the influence of outliers, this paper improved the CKF algorithm and realized an intelligent selection of robust strategies by judging the ill-conditioned matrix. Finally, a field navigation test was performed to demonstrate the effectiveness of the proposed algorithm based on the double-differenced solution mode. The experiment has proved the improved robust algorithm can greatly weaken the influence of separate, continuous, and hybrid observation anomalies for enhancing the reliability and accuracy of GNSS/INS tightly coupled navigation solutions.

Keywords: GNSS/INS integrated navigation, ambiguity resolution, Cubature Kalman filter, Robust algorithm

Procedia PDF Downloads 100

Authors: R. Premasiri, W. A. H. A. Abeygunasekara, S. M. Hewavidana, T. Jananthan, R. M. S. Madawala, K. Vaheeshan

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Forecasting the potential for disastrous events such as landslides has become one of the major necessities in the current world. Most of all, the landslides occurred in Sri Lanka are found to be triggered mostly by intense rainfall events. The study area is the landslide near Gerandiella waterfall which is located by the 41st kilometer post on Nuwara Eliya-Gampala main road in Kotmale Division in Sri Lanka. The landslide endangers the entire Kotmale town beneath the slope. Geographic Information System (GIS) platform is very much useful when it comes to the need of emulating the real-world processes. The models are used in a wide array of applications ranging from simple evaluations to the levels of forecast future events. This project investigates the possibility of developing a dynamic model to map the spatial distribution of the slope stability. The model incorporates several theoretical models including the infinite slope model, Green Ampt infiltration model and Perched ground water flow model. A series of rainfall values can be fed to the model as the main input to simulate the dynamics of slope stability. Hydrological model developed using GIS is used to quantify the perched water table height, which is one of the most critical parameters affecting the slope stability. Infinite slope stability model is used to quantify the degree of slope stability in terms of factor of safety. DEM was built with the use of digitized contour data. Stratigraphy was modeled in Surfer using borehole data and resistivity images. Data available from rainfall gauges and piezometers were used in calibrating the model. During the calibration, the parameters were adjusted until a good fit between the simulated ground water levels and the piezometer readings was obtained. This model equipped with the predicted rainfall values can be used to forecast of the slope dynamics of the area of interest. Therefore it can be investigated the slope stability of rainfall induced landslides by adjusting temporal dimensions.

Keywords: factor of safety, geographic information system, hydrological model, slope stability

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Suchitra Chnadrashekhar

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Earlier looked as a support function in an organization information technology has now become a critical utility to manage their daily operations. Organizations are processing huge amount of data which was unimaginable few decades before. This has opened the opportunity for IT sector to help industries across domains to handle the data in the most intelligent manner. Presence of IT has been a leverage for the Oil & Gas industry to store, manage and process the data in most efficient way possible thus deriving the economic value in their day-to-day operations. Proper synchronization between Operational data system and Information Technology system is the need of the hour. Predictive analytics supports oil and gas companies by addressing the challenge of critical equipment performance, life cycle, integrity, security, and increase their utilization. Predictive analytics go beyond early warning by providing insights into the roots of problems. To reach their full potential, oil and gas companies need to take a holistic or systems approach towards asset optimization and thus have the functional information at all levels of the organization in order to make the right decisions. This paper discusses how the use of predictive analysis in oil and gas industry is redefining the dynamics of this sector. Also, the paper will be supported by real time data and evaluation of the data for a given oil production asset on an application tool, SAS. The reason for using SAS as an application for our analysis is that SAS provides an analytics-based framework to improve uptimes, performance and availability of crucial assets while reducing the amount of unscheduled maintenance, thus minimizing maintenance-related costs and operation disruptions. With state-of-the-art analytics and reporting, we can predict maintenance problems before they happen and determine root causes in order to update processes for future prevention.

Keywords: hydrocarbon, information technology, SAS, predictive analytics

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: Abdalla Mohamed M. Musbahi

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The study area lies in Al-Falah area on Airport-Tripoli in Zone (16) Where planned establishment of complex multi-floors for residential and commercial, this part was divided into seven subzone. In each sup zone, were collected Orthogonal profiles by using Seismic refraction method. The overall aim with this project is to investigate the applicability of Seismic refraction method is a commonly used traditional geophysical technique to determine depth-to-bedrock, competence of bedrock, depth to the water table, or depth to other seismic velocity boundaries The purpose of the work is to make engineers and decision makers recognize the importance of planning and execution of a pre-investigation program including geophysics and in particular seismic refraction method. The overall aim with this thesis is achieved by evaluation of seismic refraction method in different scales, determine the depth and velocity of the base layer (bed-rock). Calculate the elastic property in each layer in the region by using the Seismic refraction method. The orthogonal profiles was carried out in every subzones of (zone 16). The layout of the seismic refraction set up is schematically, the geophones are placed on the linear imaginary line whit a 5 m spacing, the three shot points (in beginning of layout–mid and end of layout) was used, in order to generate the P and S waves. The 1st and last shot point is placed about 5 meters from the geophones and the middle shot point is put in between 12th to 13th geophone, from time-distance curve the P and S waves was calculated and the thickness was estimated up to three-layers. As we know any change in values of physical properties of medium (shear modulus, bulk modulus, density) leads to change waves velocity which passing through medium where any change in properties of rocks cause change in velocity of waves. because the change in properties of rocks cause change in parameters of medium density (ρ), bulk modulus (κ), shear modulus (μ). Therefore, the velocity of waves which travel in rocks have close relationship with these parameters. Therefore we can estimate theses parameters by knowing primary and secondary velocity (p-wave, s-wave).

Keywords: application of seismic, geotechnical study, physical properties, seismic refraction

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Authors: Habtamu Wolde Engudai

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Background: Sedation can be provided to facilitate a procedure or to stabilize patients admitted in pediatric intensive care unit (PICU). Sedation is often necessary to maintain optimal care for critically ill children requiring mechanical ventilation. However, if sedation is too deep or too light, it has its own adverse effects, and hence, it is important to monitor the level of sedation and maintain an optimal level. Objectives: The objective is to assess the level of sedation and associated factors among intubated critically ill children admitted to PICU of JUMC, Jimma. Methods: A prospective observation study was conducted in the PICU of JUMC in September 2021 in 105 patients who were going to be admitted to the PICU aged less than 14 and with GCS >8. Data was collected by residents and nurses working in PICU. Data entry was done by Epi data manager (version 4.6.0.2). Statistical analysis and the creation of charts is going to be performed using SPSS version 26. Data was presented as mean, percentage and standard deviation. The assumption of logistic regression and the result of the assumption will be checked. To find potential predictors, bi-variable logistic regression was used for each predictor and outcome variable. A p value of <0.05 was considered as statistically significant. Finally, findings have been presented using figures, AOR, percentages, and a summary table. Result: in this study, 105 critically ill children had been involved who were started on continuous or intermittent forms of sedative drugs. Sedation level was assessed using a comfort scale three times per day. Based on this observation, we got a 44.8% level of suboptimal sedation at the baseline, a 36.2% level of suboptimal sedation at eight hours, and a 24.8% level of suboptimal sedation at sixteen hours. There is a significant association between suboptimal sedation and duration of stay with mechanical ventilation and the rate of unplanned extubation, which was shown by P < 0.05 using the Hosmer-Lemeshow test of goodness of fit (p> 0.44).

Keywords: level of sedation, critically ill children, Pediatric intensive care unit, Jimma university

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Authors: Nur Syahidatul Idany Abdul Ghani, Rahizar Ramli, Jamilah Mohamad, Ahmad Saifizul, Mohamed Rehan Karim

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Increasing fatalities in road accidents in Malaysia over the last 10 years are quite alarming. Based on Malaysian Institute of Road Safety Research (Miros) latest research ‘Predicting Malaysian Road Fatalities for year 2020; it is predicted that road fatalities in Malaysia for 2015 is 8,780 and 10,716 for the year 2020 which 30 percent of fatalities were caused by accidents involving commercial vehicles. Government, related agencies and NGOs have continuously and persistently work to reduce the statistics through enforcement, educating the public, training to drivers, road safety campaigns, advertisements etc. However, the trend of casualties does not show encouraging pattern but instead, steadily growing. Thus, this comparative study reviews the literature pertaining on method of measurement used to evaluate commercial drivers competency. In several studies driving competency has been assessed with different assessment based on the license procedures and requirements according to the country regulation. The assessment criteria that has been establish for commercial drivers generally focus on driving tasks and assessment e.g. theory test, medical test and road assessment rather than driving competency test or physical test. Realizing the importance of specific assessment test for drivers competency this comparative study reviews the most discussed literature related to competency assessment method to identify competency of the drivers include (1. judgement and reaction, 2. skill of drivers, 3. experiences and fatigue). The concluding analysis of this paper is a comparative table for assessment methodology to access driver’s competency. A comparative study is a further discussion reviewing past literature to provide an overview on existing assessment test and potential subject matters that can be identified for further studies to increase awareness of the drivers, passengers as well as the authorities about the importance of competent drivers in order to improve safety in commercial vehicles.

Keywords: commercial vehicles, driver’s competency, specific assessment

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Authors: Nadia Akbarpour

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Pancreatic ductal adenocarcinoma is a forceful and obliterating illness, which is portrayed by intrusiveness, fast movement, and significant protection from treatment. Advances in neurotic arrangement and malignant growth hereditary qualities have worked on our illustrative comprehension of this infection; be that as it may, significant parts of pancreatic disease science remain ineffectively comprehended. A superior comprehension of pancreatic disease science should lead the way to more viable medicines. In the course of the most recent couple of years, there have been significant advances in the sub-atomic and organic comprehension of pancreatic malignancy. This included comprehension of the genomic intricacy of the illness, the job of pancreatic malignant growth undifferentiated organisms, the importance of the growth microenvironment, and the one-of-a-kind metabolic transformation of pancreas disease cells to acquire supplements under hypoxic climate. Endeavors have been made towards the advancement of the practical answer for its treatment with compelled achievement due to its complicated science. It is grounded that pancreatic malignancy undifferentiated cells (CSCs), yet present in a little count, contribute extraordinarily to PC inception, movement, and metastasis. Standard chemo and radiotherapeutic choices, notwithstanding, grow general endurance, the connected aftereffects are a huge concern. In the midst of the latest decade, our understanding with regards to atomic and cell pathways engaged with PC and the job of CSCs in its movement has expanded massively. By and by, the center is to target CSCs. The natural items have acquired a lot of thought as of late as they, generally, sharpen CSCs to chemotherapy and target atomic flagging engaged with different cancers, including PC. Some arranged investigations have demonstrated promising outcomes recommending that assessments in this course bring a ton to the table for the treatment of PC. Albeit preclinical investigations uncovered the significance of natural items in lessening pancreatic carcinoma, restricted examinations have been led to assess their part in centers. The current survey gives another knowledge to late advances in pancreatic malignancy science, treatment, and the current status of natural items in its expectation.

Keywords: pancreatic, genomic, organic, cancer

Procedia PDF Downloads 139

Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Fredrick Olatunji Ajegbomogun, Bello Modinat Morenikeji, Okorie Nancy Chituru

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Electronic information resources are highly relevant to students' academic and research needs but are grossly underutilized, despite the institutional commitment to making them available. The under-utilisation of these resources could be attributed to a low level of study behaviour coupled with a low level of computer self-efficacy. This study assessed computer self-efficacy, study behaviour, and the use of electronic information resources by students in selected polytechnics in Ogun State. A simple random sampling technique using Krejcie and Morgan's (1970) Table was used to select 370 respondents for the study. A structured questionnaire was used to collect data on respondents. Data were analysed using frequency counts, percentages, mean, standard deviation, Pearson Product Moment Correlation (PPMC) and multiple regression analysis. Results reveal that the internet (= 1.94), YouTube (= 1.74), and search engines (= 1.72) were the common information resources available to the students, while the Internet (= 4.22) is the most utilized resource. Major reasons for using electronic information resources were to source materials and information (= 3.30), for research (= 3.25), and to augment class notes (= 2.90). The majority (91.0%) of the respondents have a high level of computer self-efficacy in the use of electronic information resources through selecting from screen menus (= 3.12), using data files ( = 3.10), and efficient use of computers (= 3.06). Good preparation for tests (= 3.27), examinations (= 3.26), and organization of tutorials (= 3.11) are the common study behaviours of the respondents. Overall, 93.8% have good study behaviour. Inadequate computer facilities to access information (= 3.23), and poor internet access (= 2.87) were the major challenges confronting students’ use of electronic information resources. According to the PPMC results, study behavior (r = 0.280) and computer self-efficacy (r = 0.304) have significant (p 0.05) relationships with the use of electronic information resources. Regression results reveal that self-efficacy (=0.214) and study behavior (=0.122) positively (p 0.05) influenced students' use of electronic information resources. The study concluded that students' use of electronic information resources depends on the purpose, their computer self-efficacy, and their study behaviour. Therefore, the study recommended that the management should encourage the students to improve their study habits and computer skills, as this will enhance their continuous and more effective utilization of electronic information resources.

Keywords: computer self-efficacy, study behaviour, electronic information resources, polytechnics, Nigeria

Procedia PDF Downloads 121

Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

Procedia PDF Downloads 279

Authors: William Batson, Yunsik Song, Abel Simie

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Recent trends in suburban design and planning have centered on economic efficiency in construction and completion. In doing so, developers, builders, and architects have bypassed free and reliable sustainable solutions to minimize the carbon footprint and improve the environment. Often, suburban areas are designed without landscape features, sidewalks, parks, adequate lighting, or walking space. Much of the design concern involves minimizing construction costs and streamlining streets and utilities. A new development in creating retention ponds to mitigate flooding and slow runoff is one step in the positive direction. However, "if you build them (suburbs), they (fauna) will come." The inevitable flora and fauna that soon propagate and take refuge within these artificial retention ponds create an additional dilemma. Architects, planners, and developers know the requirements and current strategies to provide residents and wildlife with a viable and sustainable environment. This includes habitat for hibernating animals and facilitating opportunities, especially for cold-blooded mammals. Many species that migrate to these artificial ponds struggle to survive, especially during flooding and when the water table drains below the artificial rim, preventing aquatic mammals from climbing on land. This flooding often results from large areas of impervious asphalt and concrete. These impervious surfaces retain and dispense large amounts of rainwater and contaminants that carry industrial pollutants, oil, plastics, animal waste, and fertilizers into storm drains and then deposited in these retention ponds. This paper will identify and show how simple and logical solutions are used to create a sustainable suburb and reduce the carbon footprint using landscape architectural strategies and cost-free design solutions. We will also demonstrate simple changes in the present suburban design model to provide a viable and sustainable suburb for the 21st century.

Keywords: sustainavilty, suburban, flora, fauna, carbon footprint

Procedia PDF Downloads 72

Authors: Mariana Damova, Stanko Stankov, Emil Stoyanov, Hristo Hristov, Hermand Pessek, Plamen Chernev

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We will present one of the first use cases on the DestinE platform, a joint initiative of the European Commission, European Space Agency and EUMETSAT, providing access to global earth observation, meteorological and statistical data, and emphasize the good practice of intergovernmental agencies acting in concert. Further, we will discuss the importance of space-bound disruptive solutions for improving the balance between the ever-increasing water-related disasters coming from climate change and minimizing their economic and societal impact. The use case focuses on forecasting floods and estimating the impact of flood events on the urban environment and the ecosystems in the affected areas with the purpose of helping municipal decision-makers to analyze and plan resource needs and to forge human-environment relationships by providing farmers with insightful information for improving their agricultural productivity. For the forecast, we will adopt an EO4AI method of our platform ISME-HYDRO, in which we employ a pipeline of neural networks applied to in-situ measurements and satellite data of meteorological factors influencing the hydrological and hydrodynamic status of rivers and dams, such as precipitations, soil moisture, vegetation index, snow cover to model flood events and their span. ISME-HYDRO platform is an e-infrastructure for water resources management based on linked data, extended with further intelligence that generates forecasts with the method described above, throws alerts, formulates queries, provides superior interactivity and drives communication with the users. It provides synchronized visualization of table views, graphviews and interactive maps. It will be federated with the DestinE platform.

Keywords: flood simulation, AI, Earth observation, e-Infrastructure, flood forecasting, flood areas localization, response planning, resource estimation

Procedia PDF Downloads 23

Authors: Morteza Ahmadpur, Ilgin Gokasar, Saman Ghaffarian

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That Transportation system plays an important role in management of societies is an undeniable fact and it is one of the most challenging issues in human beings routine life. But by increasing the population in urban areas, the demand for transportation modes also increase. Accordingly, it is obvious that more flexible and dynamic transportation system is required to satisfy peoples’ requirements. Nowadays, there is significant increase in number of environmental issues all over the world which is because of human activities. New technological achievements bring new horizons for humans and so they changed the life style of humans in every aspect of their life and transportation is not an exception. By using new technology, societies can modernize their transportation system and increase the feasibility of their system. Real–time Taxi sharing systems is one of the novel and most modern systems all over the world. For establishing this kind of system in an urban area it is required to use the most advanced technologies in a transportation system. GPS navigation devices, computers and social networks are just some parts of this kind of system. Like carpooling, real-time taxi sharing is one of the best ways to better utilize the empty seats in most cars and taxis, thus decreasing energy consumption and transport costs. It can serve areas not covered by a public transit system and act as a transit feeder service. Taxi sharing is also capable of serving one-time trips, not only recurrent commute trips or scheduled trips. In this study, we describe the requirements and parameters that we need to establish a useful real-time ride sharing system for an urban area. The parameters and requirements of this study can be used in any urban area.

Keywords: transportation, intelligent transportation systems, ride-sharing, taxi sharing

Procedia PDF Downloads 429

Authors: Faith Eweluegim Enahoro-Ofagbe

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Worldwide, every nation is experiencing the effects of global warming. In developing countries, due to the heavy reliance on agriculture for socioeconomic growth and security, among other things, these countries are more affected by climate change, particularly with the availability of water. Floods, droughts, rising temperatures, saltwater intrusion, groundwater depletion, and other severe environmental alterations are all brought on by climatic change. Life depends on water, a vital resource; these ecological changes affect all water use, including agriculture and household water use. Therefore adequate and adaptive water usage strategies for sustainability are essential in developing countries. Therefore, this paper investigates Nigeria's challenges due to climate change and adaptive techniques that have evolved in response to such issues to ensure water management and sustainability for irrigation and provide quality water to residents. Questionnaires were distributed to respondents in the study area, central Nigeria, for quantitative evaluation of sustainable water resource management techniques. Physicochemical analysis was done, collecting soil and water samples from several locations under investigation. Findings show that farmers use different methods, ranging from intelligent technologies to traditional strategies for water resource management. Also, farmers need to learn better water resource management techniques for sustainability. Since more residents obtain their water from privately held sources, the government should enforce legislation to ensure that private borehole construction businesses treat water sources of poor quality before the general public uses them.

Keywords: developing countries, irrigation, strategies, sustainability, water resource management, water usage

Procedia PDF Downloads 116

Authors: Nitish Suvarna, Anjali Awasthi

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In an era marked by rapid technological advancements, the integration of Autonomous Vehicles into supply chain networks represents a transformative shift, promising to redefine the paradigms of logistics and transportation. This thesis delves into a comprehensive assessment of the impact of autonomous vehicles on supply chain performance, with a particular focus on network design, operational efficiency, and environmental sustainability. Employing the advanced simulation capabilities of anyLogistix (ALX), the study constructs a digital twin of a conventional supply chain network, encompassing suppliers, production facilities, distribution centers, and customer endpoints. The research methodically integrates Autonomous Vehicles into this intricate network, aiming to unravel the multifaceted effects on transportation logistics including transit times, cost-efficiency, and sustainability. Through simulations and scenarios analysis, the study scrutinizes the operational resilience and adaptability of supply chains in the face of dynamic market conditions and disruptive technologies like Autonomous Vehicles. Furthermore, the thesis undertakes carbon footprint analysis, quantifying the environmental benefits and challenges associated with the adoption of Autonomous Vehicles in supply chain operations. The insights from this research are anticipated to offer a strategic framework for industry stakeholders, guiding the adoption of Autonomous Vehicles to foster a more efficient, responsive, and sustainable supply chain ecosystem. The findings aim to serve as a cornerstone for future research and practical implementations in the realm of intelligent transportation and supply chain management.

Keywords: autonomous vehicle, agri-food supply chain, ALX simulation, anyLogistix

Procedia PDF Downloads 76

Authors: Oladipo Jimoh Ayanda, Fashagba Mathew Olasehinde

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Pension is a post retirement benefit paid to employees after retirement to cushion the effects of severance from monthly emoluments. It serves the dual purpose of providing financial succour to retired employees as well as motivating employees currently in service to greater performance on duty. However, the scheme, as operated in Nigeria, is prone to some pitfalls such as delayed and irregular payments, inadequate budgetary provisions, employee sufferings and deaths arising from the rigors of verification exercises, among others. This necessitated the replacement of the old scheme with the contributory pension scheme through an enabling law in 2004. The implementation of the new scheme has its own challenges especially in connection with administration. These challenges pose a fundamental problem of establishing a nexus between pension benefits and work performance which represent the focus of the study. The study objectives were to: determine the effect of contributory pension scheme on employees’ performance. The study population consisted of National Universities Commission recognized public and private universities in the South West Nigeria. Multi-stage sampling method involving stratified sampling and systematic sampling was used in selecting 359 respondents while data were collected through questionnaire administration. The procedure for analyzing the data included descriptive statistic, normal distribution test and cross-tabulation (gamma coefficient). The findings of the study showed that the existence of the scheme positively enhances employees’ performance as indicated by normal distribution test with Z-score (10.169) which is greater than the table value (1.96) at 0.05 level. The study concluded that the scope for enhancing employee current job performance can be quite elastic if future retirement benefits are guaranteed through proper and efficient administration and management of the contributory pension scheme. The study recommended that certain factors such as employers’ commitment which account for different levels of confidence between public and private universities should be looked into in order to improve confidence across board while the provisions of the scheme as they affect the PFAs should be properly monitored to ensure compliance.

Keywords: pension, retirement, performance, employees, benefit

Procedia PDF Downloads 332